Arguments:

log_dir: the path of the directory where to save the log files to be
parsed by TensorBoard.

histogram_freq: frequency (in epochs) at which to compute activation and
weight histograms for the layers of the model. If set to 0, histograms
won't be computed. Validation data (or split) must be specified for
histogram visualizations.

write_graph: whether to visualize the graph in TensorBoard. The log file
can become quite large when write_graph is set to True.

write_images: whether to write model weights to visualize as image in
TensorBoard.

update_freq: 'batch' or 'epoch' or integer. When using 'batch',
writes the losses and metrics to TensorBoard after each batch. The same
applies for 'epoch'. If using an integer, let's say 1000, the
callback will write the metrics and losses to TensorBoard every 1000
batches. Note that writing too frequently to TensorBoard can slow down
your training.

profile_batch: Profile the batch(es) to sample compute characteristics.
profile_batch must be a non-negative integer or a comma separated string
of pair of positive integers. A pair of positive integers signify a
range of batches to profile. By default, it will profile the second
batch. Set profile_batch=0 to disable profiling. Must run in TensorFlow
eager mode.

embeddings_freq: frequency (in epochs) at which embedding layers will be
visualized. If set to 0, embeddings won't be visualized.

embeddings_metadata: a dictionary which maps layer name to a file name in
which metadata for this embedding layer is saved. See the
details
about metadata files format. In case if the same metadata file is
used for all embedding layers, string can be passed.

Raises:

ValueError: If histogram_freq is set and no validation data is provided.